Conditional Modeling of Longitudinal Data With Terminal Event
Conditional Modeling of Longitudinal Data With Terminal Event
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DOI：
10.1080/01621459.2016.1255637
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发表时间：
2018-01-01
影响因子：
3.7
通讯作者：
Hirth, Richard
中科院分区：
文献类型：
--
作者：
Kong, Shengchun;Nan, Bin;Hirth, Richard
我们考虑了一个纵向数据的随机效应模型，其中发生了一个受右删失影响的信息终端事件。现有的方法用于分析这些数据包括联合建模方法使用潜在的脆弱性和边际估计方程的方法，使用逆概率加权;在这两种情况下的终端事件的响应变量的影响是不明确的，因此不容易解释。相比之下，我们将终末事件时间视为纵向数据条件模型中的协变量，这提供了一个简单的解释，同时保持纵向测量的响应变量和协变量之间的通常关系，远离终末事件。一个两阶段的半参数似然为基础的方法，提出了估计回归参数，首先，右删失的终端事件的时间给定其他协变量的条件分布估计，然后纵向事件给定终端事件和其他回归参数的似然函数被最大化。数值模拟和分析终末期肾病患者的医疗费用数据的方法说明。提供了理想的渐近性质。本文的补充材料可在网上查阅。
We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straightforward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided. Supplementary materials for this article are available online.